lilt-en-funsd
This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6171
- Answer: {'precision': 0.8831168831168831, 'recall': 0.9155446756425949, 'f1': 0.8990384615384616, 'number': 817}
- Header: {'precision': 0.6153846153846154, 'recall': 0.47058823529411764, 'f1': 0.5333333333333333, 'number': 119}
- Question: {'precision': 0.8849557522123894, 'recall': 0.9285051067780873, 'f1': 0.9062075215224287, 'number': 1077}
- Overall Precision: 0.8723
- Overall Recall: 0.8962
- Overall F1: 0.8841
- Overall Accuracy: 0.8029
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.2349 | 10.53 | 200 | 1.1246 | {'precision': 0.8461538461538461, 'recall': 0.8616891064871481, 'f1': 0.8538508186779866, 'number': 817} | {'precision': 0.5871559633027523, 'recall': 0.5378151260504201, 'f1': 0.5614035087719299, 'number': 119} | {'precision': 0.8546861564918314, 'recall': 0.9229340761374187, 'f1': 0.8875, 'number': 1077} | 0.8375 | 0.8753 | 0.8560 | 0.7887 |
| 0.0404 | 21.05 | 400 | 1.4684 | {'precision': 0.8298109010011123, 'recall': 0.9130966952264382, 'f1': 0.8694638694638694, 'number': 817} | {'precision': 0.5688073394495413, 'recall': 0.5210084033613446, 'f1': 0.543859649122807, 'number': 119} | {'precision': 0.879231473010064, 'recall': 0.8922934076137419, 'f1': 0.8857142857142858, 'number': 1077} | 0.8420 | 0.8788 | 0.8600 | 0.7863 |
| 0.0121 | 31.58 | 600 | 1.6187 | {'precision': 0.852112676056338, 'recall': 0.8886168910648715, 'f1': 0.8699820251647693, 'number': 817} | {'precision': 0.5775862068965517, 'recall': 0.5630252100840336, 'f1': 0.5702127659574467, 'number': 119} | {'precision': 0.8789237668161435, 'recall': 0.9099350046425255, 'f1': 0.8941605839416058, 'number': 1077} | 0.8512 | 0.8808 | 0.8657 | 0.7944 |
| 0.0071 | 42.11 | 800 | 1.6262 | {'precision': 0.8503480278422274, 'recall': 0.8971848225214198, 'f1': 0.8731387730792138, 'number': 817} | {'precision': 0.5714285714285714, 'recall': 0.47058823529411764, 'f1': 0.5161290322580646, 'number': 119} | {'precision': 0.895117540687161, 'recall': 0.9192200557103064, 'f1': 0.9070087036188731, 'number': 1077} | 0.8611 | 0.8838 | 0.8723 | 0.7929 |
| 0.0052 | 52.63 | 1000 | 1.6864 | {'precision': 0.8615751789976134, 'recall': 0.8837209302325582, 'f1': 0.8725075528700906, 'number': 817} | {'precision': 0.52, 'recall': 0.5462184873949579, 'f1': 0.5327868852459017, 'number': 119} | {'precision': 0.8842297174111212, 'recall': 0.9006499535747446, 'f1': 0.8923643054277829, 'number': 1077} | 0.8529 | 0.8728 | 0.8628 | 0.7844 |
| 0.0022 | 63.16 | 1200 | 1.5608 | {'precision': 0.8601895734597157, 'recall': 0.8886168910648715, 'f1': 0.8741721854304636, 'number': 817} | {'precision': 0.6896551724137931, 'recall': 0.5042016806722689, 'f1': 0.5825242718446602, 'number': 119} | {'precision': 0.8609215017064846, 'recall': 0.9368616527390901, 'f1': 0.897287683414851, 'number': 1077} | 0.8535 | 0.8917 | 0.8722 | 0.7973 |
| 0.0018 | 73.68 | 1400 | 1.6781 | {'precision': 0.8458904109589042, 'recall': 0.9069767441860465, 'f1': 0.8753691671588896, 'number': 817} | {'precision': 0.6129032258064516, 'recall': 0.4789915966386555, 'f1': 0.5377358490566039, 'number': 119} | {'precision': 0.874447391688771, 'recall': 0.9182915506035283, 'f1': 0.8958333333333334, 'number': 1077} | 0.8510 | 0.8877 | 0.8690 | 0.7922 |
| 0.0014 | 84.21 | 1600 | 1.7078 | {'precision': 0.8706293706293706, 'recall': 0.9143206854345165, 'f1': 0.8919402985074627, 'number': 817} | {'precision': 0.6421052631578947, 'recall': 0.5126050420168067, 'f1': 0.5700934579439252, 'number': 119} | {'precision': 0.8945454545454545, 'recall': 0.9136490250696379, 'f1': 0.9039963252181902, 'number': 1077} | 0.8729 | 0.8902 | 0.8815 | 0.8051 |
| 0.0006 | 94.74 | 1800 | 1.6171 | {'precision': 0.8831168831168831, 'recall': 0.9155446756425949, 'f1': 0.8990384615384616, 'number': 817} | {'precision': 0.6153846153846154, 'recall': 0.47058823529411764, 'f1': 0.5333333333333333, 'number': 119} | {'precision': 0.8849557522123894, 'recall': 0.9285051067780873, 'f1': 0.9062075215224287, 'number': 1077} | 0.8723 | 0.8962 | 0.8841 | 0.8029 |
| 0.0003 | 105.26 | 2000 | 1.7091 | {'precision': 0.8490990990990991, 'recall': 0.9228886168910648, 'f1': 0.8844574780058652, 'number': 817} | {'precision': 0.5959595959595959, 'recall': 0.4957983193277311, 'f1': 0.5412844036697246, 'number': 119} | {'precision': 0.8993536472760849, 'recall': 0.904363974001857, 'f1': 0.9018518518518519, 'number': 1077} | 0.8633 | 0.8877 | 0.8753 | 0.8047 |
| 0.0003 | 115.79 | 2200 | 1.6547 | {'precision': 0.8760233918128655, 'recall': 0.9167686658506732, 'f1': 0.8959330143540669, 'number': 817} | {'precision': 0.6170212765957447, 'recall': 0.48739495798319327, 'f1': 0.5446009389671361, 'number': 119} | {'precision': 0.8952122854561879, 'recall': 0.9201485608170845, 'f1': 0.9075091575091575, 'number': 1077} | 0.8745 | 0.8932 | 0.8838 | 0.8080 |
| 0.0002 | 126.32 | 2400 | 1.6868 | {'precision': 0.8633754305396096, 'recall': 0.9204406364749081, 'f1': 0.8909952606635071, 'number': 817} | {'precision': 0.6020408163265306, 'recall': 0.4957983193277311, 'f1': 0.543778801843318, 'number': 119} | {'precision': 0.8884924174843889, 'recall': 0.924791086350975, 'f1': 0.9062784349408554, 'number': 1077} | 0.8646 | 0.8977 | 0.8808 | 0.8059 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2
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Base model
SCUT-DLVCLab/lilt-roberta-en-base